Semiparametric efficient estimation for additive hazards regression with case II interval-censored survival data

Baihua He, Yanyan Liu, Yuanshan Wu, Xingqiu Zhao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

Interval-censored data often arise naturally in medical, biological, and demographical studies. As a matter of routine, the Cox proportional hazards regression is employed to fit such censored data. The related work in the framework of additive hazards regression, which is always considered as a promising alternative, remains to be investigated. We propose a sieve maximum likelihood method for estimating regression parameters in the additive hazards regression with case II interval-censored data, which consists of right-, left- and interval-censored observations. We establish the consistency and the asymptotic normality of the proposed estimator and show that it attains the semiparametric efficiency bound. The finite-sample performance of the proposed method is assessed via comprehensive simulation studies, which is further illustrated by a real clinical example for patients with hemophilia.

Original languageEnglish
Pages (from-to)1-23
Number of pages23
JournalLifetime Data Analysis
DOIs
Publication statusAccepted/In press - 10 Mar 2020

Keywords

  • Additive hazards
  • Empirical process
  • Interval-censored data
  • Semiparametric efficiency bound
  • Sieve maximum likelihood estimator
  • Survival analysis

ASJC Scopus subject areas

  • Applied Mathematics

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